Coaching based on reproductive phases

Abstract

Physiological metrics such as respiratory rate, resting heart rate, heart rate variability, temperature, and the like can be measured over time for a user and correlated to reproductive phases. By determining the chronological phase in a hormonal cycle or the like, automated recommendations for sleep, diet, exercise and the like can be provided in a phase-coordinated manner.

1 . A computer program product comprising computer executable code embodied in a non-transitory computer readable medium that, when executing on one or more computing devices, performs the steps of:
 providing a model that characterizes timewise changes during a model hormonal cycle for each of a heart rate variability, a resting heart rate, a body temperature, and a respiration rate;   acquiring physiological data for a user from a wearable monitor, wherein the physiological data includes at least heart rate data and body temperature data, and wherein the physiological data is acquired during a hormonal cycle for the user;   calculating a number of metrics for the user at least daily during the hormonal cycle, the number of metrics including at least the heart rate variability, the resting heart rate, the body temperature, and the respiration rate;   calculating an estimated cycle time for the user relative to the model hormonal cycle based on each of the number of metrics independently;   calculating a cycle time within the hormonal cycle for the user based on an ensemble of the estimated cycle times; and   providing coaching information to the user based on the cycle time.   
     
     
         2 . The computer program product of  claim 1 , wherein the hormonal cycle includes a menstrual cycle for the user. 
     
     
         3 . The computer program product of  claim 1 , wherein the hormonal cycle includes a pregnancy of the user. 
     
     
         4 . The computer program product of  claim 1 , wherein the ensemble includes a weighted average of the estimated cycle time for each of the number of metrics. 
     
     
         5 . The computer program product of  claim 1 , wherein the ensemble includes a combination of the estimated cycle time for each of the number of metrics based on a probability of accurately estimating the cycle time. 
     
     
         6 . A method comprising:
 providing a model that characterizes timewise changes during a model hormonal cycle for each of two or more physiological metrics;   acquiring heart rate data from a wearable monitor worn by a user;   calculating the two or more physiological metrics for the user at least daily based on the heart rate data;   calculating a cycle time within a hormonal cycle for the user based on an ensemble of estimated cycle times, each estimated cycle time in the ensemble derived by applying one of the physiological metrics to the model; and   providing coaching information to the user based on the cycle time.   
     
     
         7 . The method of  claim 6 , wherein the hormonal cycle includes a menstrual cycle for the user. 
     
     
         8 . The method of  claim 6 , wherein the hormonal cycle includes a pregnancy of the user. 
     
     
         9 . The method of  claim 6 , wherein the ensemble includes a weighted average of an estimated cycle time for each of the physiological metrics. 
     
     
         10 . The method of  claim 6 , wherein the ensemble includes a combination of the estimated cycle times based on a probability of accurately estimating the cycle time. 
     
     
         11 . The method of  claim 6 , wherein the ensemble includes a Bayesian model average of the estimated cycle times. 
     
     
         12 . The method of  claim 6 , wherein the ensemble includes an average of at least two of the estimated cycle times. 
     
     
         13 . The method of  claim 6 , wherein the wearable monitor includes a photoplethysmography monitor. 
     
     
         14 . The method of  claim 13 , wherein the two or more physiological metrics include at least one of a heart rate variability, a resting heart rate, and a respiration rate. 
     
     
         15 . The method of  claim 6 , wherein:
 the two or more physiological metrics include a body temperature,   the wearable monitor includes a temperature sensor,   the method includes acquiring temperature data from the temperature sensor and calculating the body temperature at least daily for the user.   
     
     
         16 . The method of  claim 6 , wherein the model hormonal cycle is derived from a population of users. 
     
     
         17 . The method of  claim 6 , wherein the model hormonal cycle is derived from a history of the user. 
     
     
         18 . A system comprising:
 a wearable monitor configured to acquire heart rate data from a user;   a model stored in a memory, the model characterizing timewise changes during a model hormonal cycle for each of two or more physiological metrics; and   a processor configured to generate a recommendation for the user by performing the steps of:
 receiving the heart rate data from the wearable monitor; 
 calculating the two or more physiological metrics for the user on a periodic basis based on the heart rate data; 
 calculating a cycle time within a hormonal cycle for the user based on an ensemble of estimated cycle times, each of the estimated cycle times derived by applying one of the physiological metrics to the model; and 
 providing coaching information to the user based on the cycle time. 
   
     
     
         19 . The system of  claim 18 , wherein the processor executes on a personal computing device of the user. 
     
     
         20 . The system of  claim 18 , wherein the processor executes on a remote server coupled to the wearable monitor through a data network. 
     
     
         21 - 60 . (canceled)